Automatic detection of user preferences for alternate user interface model

ABSTRACT

A method for automatic detection of user preferences for alternate user interface model includes operating a digital device with an active user interface model and receiving one or more input signals from a user of the digital device. The method includes comparing the one or more input signals with one or more latent user interface models and determining if one of the latent user interface models has a higher likelihood given the one or more input signals than the active user interface models. The method also includes responsively substituting the latent user interface with the highest likelihood given the one or more input signals for the active user interface model.

BACKGROUND

The present invention relates to user interfaces for digital devices,and more specifically, to automatic detection of a user preference foran alternative user interface model.

Recently people have begun to interact with and utilize a wide varietyof digital devices throughout their everyday lives. These digitaldevices include, but are not limited to, cellular phones, personal dataassistants (PDAs), e-readers, tablet computers, netbooks, self-servicekiosks, and the like. Each of these digital devices includes a userinterface model which defines the way that the users interact with thedevice. Some digital devices may include more than one user interfacemodel that the users are able to select between. Generally, a userinterface model is a mapping or correlation of inputs received from auser interface to actions or activities on the digital device.

In general, people have their own mental models of how a digital deviceshould react to various inputs from the user interface. For example, auser might envision pages of a virtual book on an electronic reader ortablet computer as organized from left-to-right. Such a user wouldlikely expect to move to the next page by pushing or sliding the currentpage from right-to-left. However, other people might bring differentmental models to the same devices. For example, another user might seethe pages as organized from top-to-bottom, and expect a downward push orslide to lead to the next page. Such differences mean that many existinguser interface models can be confusing or frustrating for at least aportion of the users of the digital device. Currently, the user can onlyovercome this conflict by adapting to the existing user interface modelof the device or by changing the user interface model of the device tomore closely align with the user's mental model.

SUMMARY

According to exemplary embodiments, a method for automatic detection ofuser preferences for an alternate user interface model includesoperating a digital device with an active user interface model andreceiving one or more input signals from a user of the digital device.The method includes comparing the one or more input signals with one ormore latent user interface models and determining if one of the latentuser interface models has a higher likelihood given the one or moreinput signals than the active user interface models. The method alsoincludes responsively substituting the latent user interface with thehighest likelihood given the one or more input signals for the activeuser interface model.

According to further exemplary embodiments, a computer program productfor automatic detection of user preferences for an alternate userinterface model includes a tangible storage medium readable by aprocessing circuit and storing instructions for execution by theprocessing circuit for performing a method. The method operating adigital device with an active user interface model and receiving one ormore input signals from a user of the digital device. The methodincludes comparing the one or more input signals with one or more latentuser interface models and determining if one of the latent userinterface models has a higher likelihood given the one or more inputsignals than the active user interface models. The method also includesresponsively substituting the latent user interface with the highestlikelihood given the one or more input signals for the active userinterface model.

A method for automatic detection of user preferences for an alternateuser interface model includes operating a user interface with an activeuser interface model and receiving one or more input signals from a userof the digital device. The method also includes calculating thelikelihood given the one or more input signals of a latent userinterface model and substituting the latent user interface with theactive user interface model if the likelihood ratio exceeds a thresholdvalue.

Additional features and advantages are realized through the techniquesof the present invention. Other embodiments and aspects of the inventionare described in detail herein and are considered a part of the claimedinvention. For a better understanding of the invention with theadvantages and the features, refer to the description and to thedrawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The subject matter which is regarded as the invention is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The forgoing and other features, and advantages ofthe invention are apparent from the following detailed description takenin conjunction with the accompanying drawings in which:

FIG. 1 is a block diagram illustrating one example of a digital devicefor practice of the teachings herein;

FIG. 2 is a flow chart illustrating the operation of a method forautomatic detection of user preferences for alternate user interfacemodel in accordance with an embodiment;

FIG. 3 is a block diagram illustrating a digital device operable forautomatic detection of user preferences for alternate user interfacemodel in accordance with an embodiment; and

FIG. 4 is a block diagram of a system for automatic detection of userpreferences for alternate user interface model in accordance with anembodiment.

DETAILED DESCRIPTION

In exemplary embodiments, a digital device is provided thatautomatically detects a user's preference of a user interface model andadapts accordingly. The digital device is provided with a plurality ofuser interface models and the user interface model that is in use isreferred to as the active user interface model. The user interfacemodels which are not currently being utilized are referred to as latentuser interface models. In general, a user interface model is a mappingof user interface events to system operations. For example, if a userswipes right across a user interface, then a swipe-right event istriggered and the digital device executes an action, or systemoperation, that is mapped to the swipe-right event. For example, in anelectronic reader a swipe-right event may be mapped to an action toadvance the page being viewed to the next page in a sequence. Inalternate user interface models, the same trigger event may be mapped toanother action or system operation. For example, in an electronic readera swipe-right event may be mapped to an action to advance the page beingviewed to the previous page in a sequence.

In exemplary embodiments, the digital device includes one or more latentuser interface models and an active user interface model. While only theactive user interface model is active at a time, the latent models maybe running in the background. The latent user interface models may stilldetect input, though they do not control the operation of the digitaldevice. In exemplary embodiments, the digital device may include amachine learning module that detects when one of the latent userinterface models has a higher likelihood given the user input, than thelikelihood of the currently employed active user interface model. Asused herein the term likelihood refers to a statistical likelihoodfunction where the likelihood of a set of parameter values given someobserved outcomes is equal to the probability of those observed outcomesgiven those parameter values. In exemplary embodiment, the likelihoodfunction may be a Bayesian likelihood function or another suitablelikelihood function.

The machine learning module of the digital device may choose to switchthe active user interface model as a result. The machine learning modelcan also incorporate additional inputs or signals to determine whichuser interface models a given user will find intuitive. Such additionalinputs or signals may include, but are not limited to, a friend'spreferences over a social network, accelerometer activity to indicatewhether the user is walking, gyroscope activity to indicate theorientation of the digital device, or the like.

In one example a digital device may be an e-book reader at a librarythat is initially set to use an active user interface model that maps aleft-right action to advance to the next page. The e-book reader maydetect that a user repeatedly gestures upward at page 1, behavior thatwould be consistent with a latent user interface model in which anup/down action is mapped to an action to advance to the next page. Afterseveral upward gestures, the e-book reader can automatically switch theactive user interface model to the latent user interface model whichmaps the upward gesture to the action of advancing the page. Inexemplary embodiments, the switching of the active and latent userinterface models on the digital device may be completely transparent, orhidden from the user, such that the user is unaware that the device hasswitched the active user interface model for a latent user interfacemodel. For instance, the user may be unaware that the device initiallyhad a left/right model, and then switched to an up/down model. In thiscase, the user experience may be as though the device had the “correct”model all along. In exemplary embodiments, the digital devices mayinclude, but are not limited to, cellular phones, personal dataassistants (PDAs), e-readers, tablet computers, netbooks, self-servicekiosks, and the like.

Turning now to FIG. 1, there is shown an embodiment of a digital device100 for implementing the teachings herein. In this embodiment, thedevice 100 has one or more central processing units (processors) 101 a,101 b, 101 c, etc. (collectively or generically referred to asprocessor(s) 101). In one embodiment, each processor 101 may include areduced instruction set computer (RISC) microprocessor. Processors 101are coupled to system memory 114 and various other components via asystem bus 113. Read only memory (ROM) 102 is coupled to the system bus113 and may include a basic input/output system (BIOS), which controlscertain basic functions of the digital device 100.

FIG. 1 further depicts an input/output (I/O) adapter 107 and a networkadapter 106 coupled to the system bus 113. I/O adapter 107 may be asmall computer system interface (SCSI) adapter that communicates with ahard disk 103 and/or tape storage drive 105 or any other similarcomponent. Hard disk 103, and tape storage device 105 are collectivelyreferred to herein as mass storage 104. Software 120 for execution onthe digital device 100 may be stored in mass storage 104. A networkadapter 106 interconnects bus 113 with an outside network 116 enablingthe digital device 100 to communicate with other such systems. A screen(e.g., a display monitor) 115 is connected to system bus 113 by displayadaptor 112, which may include a graphics adapter to improve theperformance of graphics intensive applications and a video controller.In one embodiment, adapters 107, 106, and 112 may be connected to one ormore I/O busses that are connected to system bus 113 via an intermediatebus bridge (not shown). Suitable I/O buses for connecting peripheraldevices such as hard disk controllers, network adapters, and graphicsadapters typically include common protocols, such as the PeripheralComponents Interface (PCI). Additional input/output devices are shown asconnected to system bus 113 via user interface adapter 108 and displayadapter 112. One or more user interfaces 109 may be connected to bus 113via user interface adapter 108, which may include, for example, a SuperI/O chip integrating multiple device adapters into a single integratedcircuit.

Thus, as configured in FIG. 1, the digital device 100 includesprocessing capability in the form of processors 101, storage capabilityincluding system memory 114 and mass storage 104, input means such asuser interface 109, and output capability including the display 115. Inexemplary embodiments, the user interface 109 and the display 115 may beintegrated into a single device such as a touch screen display device.In one embodiment, a portion of system memory 114 and mass storage 104collectively store an operating system such as the AIX® operating systemfrom IBM Corporation to coordinate the functions of the variouscomponents shown in FIG. 1.

Referring now to FIG. 2, a flow chart illustrating the operation of amethod for automatic detection of user preferences for alternate userinterface model in accordance with an embodiment is shown. As shown atblock 200, the method begins with operating a digital device with anactive user interface model. As used herein, operating a digital devicewith a user interface model means employing a specific user interfacemodel on the device, i.e., actively monitoring the user interface forspecific user input and mapping the detected user input to acorresponding action of the digital device. The method also includesreceiving input from a user along with other predictive signals, asshown at block 200. After the user input and predictive signals arereceived, as shown at decision block 202, the user input is comparedwith each user interface model, including both active and inactivemodels. In exemplary embodiments, the predictive signals may be anysignal that is used by the digital device to predict what user interfacemodel to use. For example, the predictive signal may be any signalindicative of the environment of the digital device, such as a GPSsignal, a signal from a gyroscope, a signal from an accelerometer, asignal from a clock, or the like. The digital device may use one or moreof these predictive signals to determine which user interface model toutilize. If the most appropriate user interface is the currently activemodel, then the operation of the device is continued with the activemodel, as shown at block 204. However, if the most appropriate userinterface is not the currently active model, then the difference betweenthe currently active model and the user input is compared to a thresholdvalue, as shown at decision block 206. If the difference exceeds thethreshold value, the operation of the device is switched to the moreappropriate model, as shown at block 208.

Referring now to FIG. 3, a block diagram illustrating a digital device300 in accordance with an exemplary embodiment is shown. The digitaldevice 300 may include an adaptive filter module 304 that is adapted tosend only selected events received from the user interface 302 to theprocessor 306 for processing. For example, the adaptive filter module304 may be designed to only forward events received from the userinterface 302 that correlate to a set of mapped actions in the activeuser interface module to the processor 306. In exemplary embodiments,the adaptive filter module 304 can be designed to forward all eventsreceived from the user interface 302 that correlate to a mapped actionin either the active user interface module or a latent user interfacemodule to the machine learning module 310. The machine learning module310 may maintain a probabilistic user interface model that it utilizesto select which user interface model should be the active user interfacemodel.

The digital device 300 may also include a user feedback module 308 thatis designed to register user reaction (e.g., annoyance) with the activeuser interface model as a feedback event that is input to the machinelearning module 310. In addition, the digital device 300 may include anexternal indicator module 312 that registers external preferences asindicator events that are input to the machine learning module 310. Theexternal indicator module 312 may be used to detect environmentalfactors that can be used select an appropriate user interface model. Forexample, one user interface model may be more appropriate when the userof the digital device 300 is walking vs. not-walking as detected by thedigital device's accelerometer.

In exemplary embodiments, the machine learning module 310 may use aprobabilistic user interface model, events received from the adaptivefilter module 304 and various external factors received from theexternal indicator module 312 and/or the user feedback module 308 toselect a user interface model that is most consistent with the eventsrecently received from the user interface 302. The machine learningmodule 310 responsively instructs the processor to 306 to utilize theselected user interface model. In exemplary embodiments, the machinelearning module 310 of the digital device 300 may consider the temporalrelationship of the events received from the adaptive filter module 304and various external factors received from the external indicator module312 and/or the user feedback module 308 to aid in the selection of theuser interface model that should be utilized by the digital device 300.

In exemplary embodiments, the machine learning module 310 may utilize athreshold value in selecting when to substitute one of the latent userinterface models for the active user interface model. The thresholdvalue may be a minimum ratio of the likelihood of a latent model, to thelikelihood of the current model, necessary to motivate a switch. Inexemplary embodiments, the threshold value can be modified by themachine learning module 310 based on environmental factors and basedupon the usage history of the digital device. For example, the machinelearning module 310 may utilize a lower threshold value when the digitaldevice 300 has been dormant, or unused, for an specified period of time,referred to as a threshold time period. In another example, the machinelearning module 310 may utilize a higher threshold value when thedigital device 300 has been in continual use.

In exemplary embodiments, the digital device 300 may be designed todisplay one or more user interface control options to the user. The oneor more user interface control options may include, but are not limitedto, a revert button, a default button, a save button or the like. Forexample, the digital device 300 may automatically display a revertbutton, via a user interface or a display, for a set amount of timeafter the user interface model of the digital device 300 is changed. Byselecting the revert button the user can instruct the digital device 300to change back to the previous user interface model. In another example,the digital device may display a save default or save button to theuser, which can be used to set the current user interface model to thedefault user interface model or to save and label the current userinterface model.

Referring now to FIG. 4, a system 400 for automatic detection of userpreferences for alternate user interface model in accordance with anembodiment is shown. The system 400 includes one or more digital devices402 in communication with a server 404 via communications network 406.The digital devices 402 each include a user interface 408 for receivinginput from a user and a communications adapter 410 for communicatingwith the server 404. The server 404 includes a user interface modeldatabase 412 and a communications adapter 414. The digital devices 402each receive input via the user interface 408 and communicate thereceived input to the server 404. The server 404 compares the receiveduser input with a plurality of user interface models stored in the userinterface model database 412 and determines the user interface modelwith the highest likelihood given the received user input. After thedetermination of the appropriate user interface model is made, theserver 404 may instruct the digital device 402 to use the selected userinterface model.

In one embodiment, the digital device may be an electronic readingtablet with a touch screen user interface. The electronic reading tabletmay be designed to have a default active user interface model whichcorrelates a right-to-left user swipe to a command to advance the pageof the book displayed. In addition, the electronic reading tablet mayhave a latent user interface model with correlates a bottom-to-top userswipe to a command to advance the page of the book displayed. When auser first picks up the electronic reading tablet, it will be operatingwith the default active user interface model. If the user repeatedlypreforms a bottom-to-top swipe, the electronic reading tablet maydetermine that the latent user interface model better fits the user'sinput and then switch to operating with the latent user interface model.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described below with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of onemore other features, integers, steps, operations, element components,and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

The flow diagrams depicted herein are just one example. There may bemany variations to this diagram or the steps (or operations) describedtherein without departing from the spirit of the invention. Forinstance, the steps may be performed in a differing order or steps maybe added, deleted or modified. All of these variations are considered apart of the claimed invention.

While the preferred embodiment to the invention had been described, itwill be understood that those skilled in the art, both now and in thefuture, may make various improvements and enhancements which fall withinthe scope of the claims which follow. These claims should be construedto maintain the proper protection for the invention first described.

What is claimed is:
 1. A method, comprising: operating, by a processer,a digital device with an active user interface model, wherein thedigital device includes a touchscreen that displays a user interfacecomprising a layout, and wherein the active user interface model is afirst mapping of input gestures to a first set of operations to beexecuted by a processor of the digital device in response; receiving arepeated series of at least one input gesture from a user of the digitaldevice via the touchscreen; determining a first likelihood ratio bycomparing the series of input gestures with the first set of inputgestures associated with the active user interface model; determining asecond likelihood ratio by comparing the series of input gestures with asecond set of input gesture associated with a latent user interfacemodel, wherein the latent user interface model is a second mapping ofinput gestures to a second set of operations to be executed by theprocessor of the digital device in response, distinct from the first setof operations; determining the higher likelihood ratio from the firstlikelihood ratio and the second likelihood ratio; substituting thelatent user interface model for the active user interface model, inresponse to the second likelihood ratio being higher, whereinsubstituting the latent user interface model does not alter the layoutof the user interface; and in response to substituting the latent userinterface model for the active user interface model, displaying a revertbutton on the touchscreen, wherein in response to a selection of therevert button the digital device reverts to the active user interfacemodel.
 2. The method of claim 1, wherein the latent user interface modelis a plurality of latent user interface models, and the method furthercomprises, determining if one of the latent user interface models has ahigher likelihood ratio than the first likelihood ratio of the activeuser interface models, wherein determining a likelihood ratio includesrecognizing statistical properties of a sequence of user inputs thatcorresponds to one of the latent user interface models.
 3. The method ofclaim 1, further comprising notifying the user before substituting thelatent user interface model with the highest likelihood given the seriesof input gestures for the active user interface model.
 4. The method ofclaim 1, wherein the series of input gestures are limited to inputgestures received during a threshold time period.
 5. The method of claim1, further comprising soliciting input from a user to determine aninitial user interface model to be used as the active user interfacemodel.
 6. The method of claim 1, further comprising receiving one ormore predictive signals for use in determining if the latent userinterface model has a higher likelihood ratio given the series of inputgestures than the active user interface model.
 7. The method of claim 1,further comprising displaying a user interface control options to theuser in response to substituting the latent user interface model for theactive user interface model.
 8. A computer program product for automaticdetection of user preferences for an alternate user interface model, thecomputer program product comprising: a non-transitory computer readablestorage medium for storing instructions for execution by a processingcircuit for performing a method comprising: operating a digital devicewith an active user interface model, wherein the digital device includesa touchscreen that displays a user interface comprising a layout, andwherein the active user interface model is a first mapping of inputgestures to a first set of operations to be executed by a processor ofthe digital device in response; receiving a repeated series of at leastone input gesture from a user of the digital device via the touchscreen;determining a first likelihood ratio by comparing the series of inputgestures with the first set of input gestures associated with the activeuser interface model; determining a second likelihood ratio by comparingthe series of input gestures with a second set of input gestureassociated with a latent user interface model, wherein the latent userinterface model is a second mapping of input gestures to a second set ofoperations to be executed by the processor of the digital device inresponse, distinct from the first set of operations; determining thehigher likelihood ratio from the first likelihood ratio and the secondlikelihood ratio; substituting the latent user interface model for theactive user interface model, in response to the second likelihood ratiobeing higher, wherein substituting the latent user interface model doesnot alter the layout of the user interface; and in response tosubstituting the latent user interface model for the active userinterface model, displaying a revert button on the touchscreen, whereinin response to a selection of the revert button the digital devicereverts to the active user interface model.
 9. The computer programproduct of claim 8, wherein the latent user interface model is aplurality of latent user interface models, and the method furthercomprises, determining if one of the latent user interface models has ahigher likelihood ratio than the first likelihood ratio of the activeuser interface models, wherein determining a likelihood ratio includesrecognizing statistical properties of a sequence of user inputs thatcorresponds to one of the latent user interface models.
 10. The computerprogram product of claim 8, further comprising notifying the user beforesubstituting the latent user interface model with the highest likelihoodgiven the series of input gestures for the active user interface model.11. The computer program product of claim 8, wherein the series of inputgestures are limited to input gestures received during a threshold timeperiod.
 12. The computer program product of claim 8, further comprisingsoliciting input from a user to determine an initial user interfacemodel to be used as the active user interface model.
 13. The computerprogram product of claim 8, further comprising receiving one or morepredictive signals for use in determining if the latent user interfacemodel has a higher likelihood ratio given the series of input gesturesthan the active user interface model.
 14. The computer program productof claim 8, further comprising displaying a user interface controloption after substituting the latent user interface model for the activeuser interface model.
 15. A method for operating a digital devicecomprising: operating a user interface of the digital device with anactive user interface model, wherein the digital device includes atouchscreen that displays a user interface comprising a layout, andwherein the active user interface model is a first mapping of said userinterface to a first set of operations to be executed by a processor ofthe digital device in response to input gestures from the user;receiving a repeated series of at least one input gesture from a user ofthe digital device via the user interface; calculating a correlationvalue between the series of input gestures with a latent user interfacemodel wherein the latent user interface model is a second mapping ofsaid user interface to a second set of operations to be executed by theprocessor of the digital device in response to the input gestures, thesecond set of operations being distinct from the first set ofoperations; substituting the active user interface model with the latentuser interface model in response to the correlation value exceeding athreshold value, wherein substituting the latent user interface modeldoes not alter the layout of the user interface; and in response tosubstituting the latent user interface model for the active userinterface model, displaying a revert button on the touchscreen, whereinin response to a selection of the revert button the digital devicereverts to the active user interface model.
 16. The method of claim 15,wherein the threshold value is adjusted to a minimum value when thedigital device has been dormant for a threshold time period.
 17. Themethod of claim 15, further comprising notifying the user beforesubstituting the active user interface model with the latent userinterface model.
 18. The method of claim 15, further comprisingsoliciting input from a user to determine an initial user interfacemodel to be used as the active user interface model.
 19. The method ofclaim 15, further comprising displaying a user interface control optionafter substituting the latent user interface model for the active userinterface model.